chundoong-lab-ta/SamsungDS22/submissions/final/seongje.jang/A/convolution.cpp

#include "convolution.h"
#include "util.h"
#include <mpi.h>
#include <stdio.h>
#include <immintrin.h>

static float *input, *input_col, *output, *filter;
static int N, C, H, W;
static int K, R, S;
static int OH, OW;
static int pad;
static int dilation;
static int stride;
static int mpi_rank, mpi_world_size;

#define MAX_NODES (2)
static int ns[MAX_NODES], ne[MAX_NODES];

inline int min(int a, int b) { return a < b ? a : b; }

#define ITILESIZE (4)
#define JTILESIZE (4096)
#define KTILESIZE (4096)

void mat_mul_omp(const float *A, const float *B, 
		const int MM, const int NN, const int KK,
		float *C) {
	#pragma omp parallel for num_threads(20) schedule(dynamic)
	for (int ii = 0; ii < MM; ii += ITILESIZE) {
		for (int jj = 0; jj < NN; jj += JTILESIZE) {
			for (int kk = 0; kk < KK; kk += KTILESIZE) {

				for (int k = kk; k < min(KK, kk + KTILESIZE); k++) {
					for (int i = ii; i < min(MM, ii + ITILESIZE); i++) {
						float ar = A[i * KK + k];
						for (int j = jj; j < min(NN, jj + JTILESIZE); j++) {
							C[i * NN + j] += ar * B[k * NN + j];
						}
					}
				}
			}
		}
	}
}

void im2col_cpu(const float* data_im, const int channels,
		const int height, const int width, const int kernel_h, const int kernel_w,
		const int pad_h, const int pad_w,
		const int stride_h, const int stride_w,
		const int dilation_h, const int dilation_w,
		float* data_col) {
  int channels_col = channels * kernel_h * kernel_w;
  #pragma omp parallel for num_threads(20) schedule(dynamic)
  for (int c = 0; c < channels_col; ++c) {
    int w_offset = c % kernel_w;
    int h_offset = (c / kernel_w) % kernel_h;
    int c_im = c / kernel_h / kernel_w;

    const int hc0 = h_offset * dilation_h - pad_h;
    const int wc0 = w_offset * dilation_w - pad_w;
    for (int h = 0; h < OH; ++h) {
      int h_pad = h * stride_h + hc0;

      const int row_offset = (c * OH + h) * OW;
      const int srow_offset = (c_im * height + h_pad) * width;
      for (int w = 0; w < OW; ++w) {
        int w_pad = w * stride_w + wc0;
        if ((((unsigned)h_pad) < ((unsigned)height)) && (((unsigned)w_pad) < ((unsigned)width)))
          data_col[row_offset + w] = data_im[srow_offset + w_pad];
        else {
          data_col[row_offset + w] = 0.;
        }
      }
    }
  }
}

void convolution(
		float *_input, float *_output, float *_filter, 
		int _N, int _C, int _H, int _W,
		int _K, int _R, int _S,
		int _pad, int _dilation, int _stride) {
	input = _input;
	output = _output;
	filter = _filter;

	if(mpi_rank != 0) {
		alloc_tensor(&input, N, C, H, W);
		alloc_tensor(&output, N, K, OH, OW);
		alloc_tensor(&filter, K, C, R, S);
		zero_tensor(output, N, K, OH, OW);
	}

	// Scatter input
	if(mpi_rank == 0) {
		for(int i=1; i < mpi_world_size; i++) {
			MPI_Send(input + ns[i]*C*H*W, (ne[i]-ns[i])*C*H*W, MPI_FLOAT, i, 0,
					MPI_COMM_WORLD);
		}
	} 
	else {
		MPI_Recv(input + ns[mpi_rank]*C*H*W, (ne[mpi_rank]-ns[mpi_rank])*C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, nullptr);
	}

	// Broadcast filter
	MPI_Bcast(filter, K*C*R*S, MPI_FLOAT, 0, MPI_COMM_WORLD);

	for(int n=ns[mpi_rank]; n<ne[mpi_rank]; n++) {
		im2col_cpu(input + n*C*H*W, C, H, W, R, S, pad, pad, 
				stride, stride, dilation, dilation, input_col);
		mat_mul_omp(filter, input_col, K, OH*OW, R*S*C, output + n*K*OH*OW); 
	}

	// Gather output
	if(mpi_rank == 0) {
		for(int i=1; i < mpi_world_size; i++) {
			MPI_Recv(output + ns[i]*K*OH*OW, (ne[i]-ns[i])*K*OH*OW, MPI_FLOAT, i, 0,
					MPI_COMM_WORLD, nullptr);
		}
	}
	else {
		MPI_Send(output + ns[mpi_rank]*K*OH*OW, (ne[mpi_rank]-ns[mpi_rank])*K*OH*OW,
				MPI_FLOAT, 0, 0, MPI_COMM_WORLD);
	}
}

void convolution_init(
		int _N, int _C, int _H, int _W,
		int _K, int _R, int _S,
		int _pad, int _dilation, int _stride) {
	N = _N; C = _C; H = _H; W = _W;
	K = _K; R = _R; S = _S;
	pad = _pad;
	dilation = _dilation;
	stride = _stride;

	MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
	MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);

	OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
	OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
	input_col = (float*)malloc((size_t)R*S*C*OH*OW*sizeof(float));
	if(!input_col) {
		printf("memory alloc failed\n");
		exit(0);
	}

	for(int i=0; i < mpi_world_size; i++) {
		ns[i] = N/mpi_world_size *i;
		ne[i] = N/mpi_world_size*(i+1);
	}
	ne[mpi_world_size-1] = N;
}

void convolution_final(
		int _N, int _C, int _H, int _W,
		int _K, int _R, int _S,
		int _pad, int _dilation, int _stride) {
}